Tuning the reactivity of carbon surfaces with oxygen-containing functional groups

Oxygen-containing carbons are promising supports and metal-free catalysts for many reactions. However, distinguishing the role of various oxygen functional groups and quantifying and tuning each functionality is still difficult. Here we investigate the role of Brønsted acidic oxygen-containing functional groups by synthesizing a diverse library of materials. By combining acid-catalyzed elimination probe chemistry, comprehensive surface characterizations, 15N isotopically labeled acetonitrile adsorption coupled with magic-angle spinning nuclear magnetic resonance, machine learning, and density-functional theory calculations, we demonstrate that phenolic is the main acid site in gas-phase chemistries and unexpectedly carboxylic groups are much less acidic than phenolic groups in the graphitized mesoporous carbon due to electron density delocalization induced by the aromatic rings of graphitic carbon. The methodology can identify acidic sites in oxygenated carbon materials in solid acid catalyst-driven chemistry.

The surface area, pore size, and volume are nearly the same under various treatments, indicating that most of the OCFGs are on the outer surface of the carbon. Concentrated nitric acid changes the carbon structure features.

Supplementary Note 1 C 1s and O 1s fitting parameters
It was reported that the C 1s features could be deconvoluted into multiple peaks representing chemical states of carbon trigonal geometry. 1,2 Graphitized carbon has a unique asymmetric peak near 284.4 eV interpreted as sp 2 carbon (C=C). 3 Specifically, the FWHM of sp 2 is narrower than other species due to the highly graphitized C=C structure and the monochromatic aluminum Kα X-ray source (300-400 nm) we use for the measurement. For the rest of the species, the FWHM is the same and is constrained to 0.5-1 eV but can be broader than sp 2 . The symmetric peak at approximately 284.8 eV is assigned to sp 3 carbon (C-C). 4 The contribution of oxygen in the C 1s peak higher than 285 eV corresponds to multiple OCFGs. The C-O single-bonded groups at lower binding energy around 285.5 eV are assigned to the alcoholic, phenolic (hydroxyl), and ethers groups. The C-O at higher binding energy near 286.2 eV is attributed to ketoenolic equilibria or furans. Other highly oxidized peaks at 287.4 eV are assigned to C=O double bonds like ketone and quinones. The binding energy at around 288.7 eV is assigned to -COO (carboxylic or lactones). 5 Satellite features, which are also known as shake-up, are due to π-π* (>290 eV) transitions in which the electrons in the highest occupied molecular orbitals are promoted to the lowest unoccupied molecular orbitals. 6,7 To make the analysis more accurate, we combine the analysis of O 1s and C 1s spectra. Based on the linewidth of the O 1s spectra and the recommendation of fitting parameters for the graphitic carbon materials, 8 fitting with 5 to 6 peaks with an average FWHM of at least 1.0-2.0 eV per peak is feasible for the graphitized carbon material. The detailed peak assignment is summarized in Table S3 and Table S4.
These fittings were consistently used for all spectra, and the residual standard deviation (STD) was below 5% (closer to 1%). The C 1s and O 1s spectra peak assignments were based on literature. 6,8,13 Due to the high complexity of the O 1s spectrum, the core-level O 1s spectra fitting was performed on the O 1s difference spectra (Fig. 2b) obtained from the samples annealed at different temperatures. This allows one to precisely distinguish the O 1s spectrum of thermally unstable from thermally more stable species.
Care was taken that all the fittings were self-consistent and could be compared quantitatively.

Monte Carlo error analysis of C 1s and O 1s peak fitting models
To estimate the errors in the fitting model and the uncertainties of the fitting parameters, we did a comprehensive Monte Carlo error analysis of our fitting models. Monte Carlo error analysis for peak models provides an error estimate for the precision with which the optimization process determines fitting parameters given the expected noise in the data. The error analysis was performed by CASA XPS (2.3.22 PR1.0) software.  For C 1s spectra fittings, the STD DEV of the peak position/FWHM of different species in each sample is on the same order of magnitude and below (or close) 5% (closer to 1%), suggesting the fitting parameters and the fitting model is reasonable and stable.   For C 1s spectra fittings, the Monte Carlo error matrix of the peak position/FWHM between different species in each sample is almost on the same order of magnitude and below 1%, further indicating the fitting parameters and the fitting model is reasonable and stable. For O 1s spectra fittings, the STD DEV of the peak position/FWHM of different species in each sample is on the same order of magnitude and below (or close) 5% (closer to 1%), suggesting the fitting parameters and fitting model is reasonable and stable. For O 1s spectra fittings, the Monte Carlo error matrix of the peak position/FWHM between different species in each sample is on the same order of magnitude and below 1%, further indicating the fitting parameters and the fitting model is reasonable and stable. The overall fitting residual STD of C 1s spectra for each sample is lower than 5%, which is acceptable, and the error might result from the line shape of sp 2 carbon which is unavoidable.
The overall fitting residual STD of O 1s spectra for each sample is lower than 5% (closer to 1%), indicating a stable fitting model.

Supplementary Note 2 Surface OFCGs concentration correction
The photoelectrons irradiated in XPS cannot pass through more than about 4 nm in depth, 10 therefore, the oxygen content from the XPS is the surface component. The CHNS (Table S16) was conducted to quantify the oxygen content from the bulk, and correct the OFCGs concentration with the equations (equation 1 and 2): Where Os/Cs is determined by XPS, and Ob/Cb is calculated by CHNS measurements. The unit of Osurface and Csurface is (gC/gcat) means the concentration of O and C in the surface layer that XPS can detect from the outer surface. Five points were eliminated from the 16 points due to the large error bar of the CHNS result (Table S1, Fig. S6). Finally, the corrected O and C concentrations (Table S14)

Quantification of individual intrinsic oxygen functional groups (Gi)
To determine the concentration of intrinsic Gi (molGi/gcat) ( Figure S7) from OCFGs (molCj/gcat or molOj/gcat) obtained from the XPS spectra fitting and corrected by CHNS, we solve the balance equations shown below, The matrix of kij is shown in Table S15. To ensure a full rank matrix of kij, G1 (the pyrone group) and G8 (the lactol group) are removed since G1 is a base group rarely generated and G8 is unstable under typical conditions. We then solve for the values of Gi using eq. (1). The ordinary least square (OLS) regression was first employed but showed negative values of Gi which are not physically possible (Table S16). Hence, non-negative least square (NNLS) regression was used to obtain non-negative values of Gi (Table S17). However, the values of G9 throughout the 11 samples are zero, indicating that G9 does not exist in our samples. Thus, we removed G9. The R 2 values of NNLS regression are generally the same as those of the OLS regression, suggesting that it is feasible to have non-negative values (Table S18). Standardizing the initial dehydration rate and Gi (Table S17, resolved from the NNLS regression) was performed by z-score normalization (Table S19). We performed the data analysis using the Gi obtained from the NNLS regression (Table S17).     The Gi values are used for performing the individual regression to obtain the R square of the scatter plot of the initial dehydration rate against the concentration of OCFGs (Fig. 3d).  The data in Table S19 was standardized based on the values from the Table S17 (solved by the NNLS regression). The Partial least square (PLS) analysis was performed using the data in Table S19. To perform Bayesian inference, we generate a Markov chain Monte Carlo estimation of the posterior using the No-U-Turn Sampler of the Stan software. 11,12 Specifically, we obtain 20,000 samples from the posterior distribution of the linear regression relating measured reaction rates with each of our active site candidate's concentrations. For each of these 20,000 sampled regressions, we evaluate the root-mean-squared error (RMSE) and plot the resulting RMSE distributions in Figure S9 for three of the acidic active site candidates. With the 95% credible intervals of the hydroxyl/phenolic site showing the lowest RMSE range and not overlapping with any alternative candidates, this site offers the best linear regression with the measured reaction rates, and we can statistically conclude that this is the best active site candidate.
The Bayesian analysis was performed using the data in Table S19. The oxygen content decreased with time on stream when the samples were heated to 250 °C, suggesting the thermal decomposition of the functional groups (e.g., carboxylic, or phenolic/hydroxyl groups).

Figure S11
Figure S11. In situ XPS of GMCs-ox(24h)-400 ºC with thermal stability measurements. a XPS survey scans of the fresh and spent samples. b atomic oxygen content under different annealing temperatures with the spectra scan every 30 min. c C 1s core-level region at different treatment temperatures (room temperature, 140 °C, 250 °C, 400 °C, 500 °C). The spectra were collected every 30 min. d O 1s core-level spectra with different annealing temperatures. Spectra were collected every 30 min.
The oxygen content decreased with time on stream when the samples were heated to 250 °C, suggesting the thermal decomposition of the functional groups (e.g., carboxylic, or phenolic/hydroxyl groups). The decreasing rate of the oxygen content in UHV is faster than in IPA, indicating that IPA was unavoidably adsorbed on carbon surfaces during the reaction. Compared with GMCs-ox(24h)-400 °C, GMCs-ox(48h)-600 °C have identical oxygen content but different reactivity and thermal stability. However, when exposed to IPA, the oxygen content remains unchanged, indicating no reaction on the sample surfaces. Furthermore, OCFGs are thermally stable at 250 °C during the reaction due to low concentrations of -OH and -COOH on the sample surfaces, consistent with the IPA dehydration ( Figure S13) and TPDE-MS results (Fig. 2a).  Figure S13 depicts the 2-propanol dehydration over samples with identical oxygen content but different acidic OCFGs distributions. Clearly, the propene yield is low on GMCs-ox(48h)-600 ºC due to low -OH and -COOH concentration.  It is challenging to analyze the dynamic change of OCFGs of the spent catalysts during reactions due to following reasons: (1) The reaction rate cannot be monitored on the NAP-XPS instrument.
(2) Ex situ XPS measurement of the spent samples cannot prevent complete contamination from air even with the air-free vacuum transfer module. As shown in Figure S15c, the oxygen content increases with time-on-stream, suggesting H2O and alcohol originating from the reactant and products or some contamination from air adsorbed on the spent catalysts.  Compare the fresh sample with the steamed one, the oxygen content and the distribution of the OCFGs are almost the same, indicating that the in situ formed carboxylic groups (from hydrolysis of carboxylic anhydride by steaming in 0.1 bar H2O at 250 °C) compensate for the thermal decomposition of the functional groups (Fig. 3e).

Supplementary Note 4 Estimation of TOF of active centers
The number of -OH (phenolic/hydroxyl groups) and -COOH (carboxylic groups) are estimated from ex situ C 1s and O 1s spectra deconvolution and the Gi calculated by data analysis (see Methods, Supplementary Note 3). Specifically, the oxygen content of the surface is comparable to the bulk due to the distribution of oxygen atoms is perfectly uniform in the depth direction (Fig. S6) To validate that -OH is the dominant active site, we estimated the TOFs on the active centers from the statistical perspective. We use one 2-parameter model (A), supported by our data and three 1-parameter models by assuming that (1) all exposed -OH and -COOH are on the outer surfaces of carbon ( Model D shows the lowest Adj. R 2 value, suggesting that -COOH alone cannot fully capture the variability of the rate. In contrast, Model C shows a higher Adj. R 2 value, indicating that -OH is a better active site candidate than -COOH. Besides, the Adj.R 2 values of Model A and Model B are slightly lower than those of Model C due to the overfitting after adding the -COOH term, further implying the poor correlation between -COOH and dehydration rate. More importantly, the confidence interval (Table S20) of -COOH in Model A is between -0.509 and 0.573 which includes 0, indicating the possibility of zero-TOF of -COOH. In other words, compared to -OH, -COOH has a negligible contribution to dehydration activity. Overall, the TOF fitting results based on the 4 models demonstrate that -OH is the best active site for the alcohol dehydration reaction.       We further conducted the NMR with the loadings above monolayer (the samples treated with liquid 15 N ACN) (Fig. S23 a, b) to distinguish the acid sites with different acidity. In Fig. S23b, with the increase of the evaporation time, the peaks disappear with the order of the ACN desorption rate. There is only one broad peak left after 60 min evaporation. Therefore, the assignments of the peaks can be identified by the combination of ACN desorption rate and our DFT calculations (Fig. 4f). The four peaks are i) pseudo-liquid ACN, ii) physically absorbed ACN, iii) strongly physically absorbed ACN & ACN absorbed on weak acid sites (most probably the -OH associated with the alkyl rings). iv) ACN absorbed on strong protonated acid sites (including -OH/-COOH associated with different numbers of benzene rings), respectively. The very broad last peak suggests that the strong acid sites are overlapped due to the complex carbon microenvironment.
To resolve the overlapping of the acid sites of the last broad peak in Fig S23b, the measurement at cryogenic temperatures (-25 °C ~ -75 °C to freeze ACN) is performed. In Fig. S24, the peak width is gradually narrower with the decreasing of the measurement temperature, however, no more peaks were observed.
We concluded that the peaks assigned to the strong acid sites should be the overlapping of the different acidic protonated OCFGs, including -COOH associated with alkyl rings or benzene rings but with fewer ring numbers or -OH associated with a greater number of benzene rings (phenolic groups).